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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

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Showing 631–640 of 655 papers

TitleStatusHype
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
Bayesian Quantile and Expectile Optimisation—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Belief Flows of Robust Online Learning—0
Best Arm Identification in Batched Multi-armed Bandit Problems—0
Active RLHF via Best Policy Learning from Trajectory Preference Feedback—0
Better Optimism By Bayes: Adaptive Planning with Rich Models—0
Blind Exploration and Exploitation of Stochastic Experts—0
Bootstrapped Thompson Sampling and Deep Exploration—0
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